Search Results - (( new location based algorithm ) OR ( web application ((use algorithm) OR (tree algorithm)) ))
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Named entity recognition using a new fuzzy support vector machine.
Published 2008“…Some of the Machine learning algorithms used in NER methods are, support vector machine(SVM), Hidden Markov Model, Maximum Entropy Model (MEM) and Decision Tree. …”
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Intelligent cooperative web caching policies for media objects based on decision tree supervised machine learning algorithm
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A new split based searching for exact pattern matching for natural texts
Published 2018“…Exact pattern matching algorithms are popular and used widely in several applications, such as molecular biology, text processing, image processing, web search engines, network intrusion detection systems and operating systems. …”
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An optimized attack tree model for security test case planning and generation
Published 2018“…By leveraging on the optimized attack tree algorithm used in this research work, the threat model produces efficient test plans from which adequate test cases are derived to ensure a secured web application is designed, implemented and deployed. …”
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A Data Mining Approach to Construct Graduates Employability Model in Malaysia
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A Proposed False Report Identification Algorithm for a Mobile Application in the IoT Environment
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Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform
Published 2009“…The development of this architecture is based on several programming language as it involves algorithm implementation on C, parallelization using Parallel Virtual Machine (PVM) and Java for web services development. …”
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Car dealership web application
Published 2022“…The used car dealership web application was implemented with ASP.NET Core. …”
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An improved framework for content and link-based web spam detection: a combined approach
Published 2021“…The content-based web spam detection framework uses three proposed and two improved content-based algorithms for web spam detection. …”
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Cyberbullying detection: a machine learning approach
Published 2022“…Machine learning is a hot topic and it is widely implemented in software, web application and more. Those algorithms are used in the classification or regression model to predict an input. …”
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Defect green coffee bean detection using image recognition and supervised learning
Published 2022“…Therefore, in this research project, the process will be conducted by using an image classifier with the model of a machine learning algorithm which the candidates comprise of Support Vector Machine, k-Nearest Neighbour and Decision Tree. k-nearest neighbour has the highest F1-score (0.51) than the other two algorithms (Support Vector Machine: 0.50, and Decision Tree: 0.48). …”
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Content-based recommender system for an academic social network / Vala Ali Rohani
Published 2014“…The algorithm exploits all interests and preferences in a hierarchy tree structure. …”
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Comparative study of machine learning algorithms in data classification
Published 2025“…The results will help with real-world data mining applications of machine learning and be a useful guide for further study and practical applications.…”
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An Automated System For Classifying Conference Papers
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Context-Aware Recommender System based on machine learning in tourist mobile application / Nor Liza Saad … [et al.]
Published 2022“…Furthermore, data were collected simulated based on the mobile application prototype to be used for finding the suitable machine learning algorithms in the recommendation system module. …”
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Extreme learning machine for user location prediction in mobile environment
Published 2011“…Findings – WiFi’s SS contributes more in accuracy to the prediction of user location than WiFi’s SQ. Moreover, the new framework based on ELM has been compared with the k-Nearest Neighbor and the results have shown that the proposed model based on the extreme learning algorithm outperforms the k-Nearest Neighbor approach. …”
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Extreme learning machine for user location prediction in mobile environment
Published 2011“…Findings – WiFi's SS contributes more in accuracy to the prediction of user location than WiFi's SQ. Moreover, the new framework based on ELM has been compared with the k-Nearest Neighbor and the results have shown that the proposed model based on the extreme learning algorithm outperforms the k-Nearest Neighbor approach. …”
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